library_name: transformers
base_model: meta-llama/Meta-Llama-3-8B-Instruct
license: llama3
language:
- pt
tags:
- code
- sql
- finetuned
- portugues-BR
Lloro SQL
Lloro SQL, developed by Semantix Research Labs, is a language Model that was trained to effectively transform Portuguese queries into SQL Code. It is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct, that was trained on Bird and Spider public datasets. The fine-tuning process was performed using the QLORA metodology on a GPU A100 with 40 GB of RAM.
Model description
Model type: A 7B parameter fine-tuned on GretelAI public datasets.
Language(s) (NLP): Primarily Portuguese, but the model is capable to understand English as well
Finetuned from model: meta-llama/Meta-Llama-3-8B-Instruct
What is Lloro's intended use(s)?
Lloro is built for Text2SQL in Portuguese contexts .
Input : Text
Output : Text (Code)
Usage
Using an OpenAI compatible inference server (like vLLM)
from openai import OpenAI
client = OpenAI(
api_key="EMPTY",
base_url="http://localhost:8000/v1",
)
def generate_responses(instruction, client=client):
chat_response = client.chat.completions.create(
model=<model>,
messages=[
{"role": "system", "content": "Você escreve a instrução SQL que responde às perguntas feitas. Você NÃO FORNECE NENHUM COMENTÁRIO OU EXPLICAÇÃO sobre o que o código faz, apenas a instrução SQL terminando em ponto e vírgula. Você utiliza todos os comandos disponíveis na especificação SQL, como: [SELECT, WHERE, ORDER, LIMIT, CAST, AS, JOIN]."},
{"role": "user", "content": instruction},
]
)
return chat_response.choices[0].message.content
output = generate_responses(user_prompt)
Params Training Parameters
| Params | Training Data | Examples | Tokens | LR |
|---|---|---|---|---|
| 8B | GretelAI public datasets | 65000 | 18.000.000 | 9e-5 |
Model Sources
GretelAI: https://huggingface.co/datasets/gretelai/synthetic_text_to_sql
Performance
| Modelo | LLM as Judge | Code Bleu Score | Rouge-L | CodeBert- Precision | CodeBert-Recall | CodeBert-F1 | CodeBert-F3 |
|---|---|---|---|---|---|---|---|
| Llama 3 - Base | 65.48% | 0.4583 | 0.6361 | 0.8815 | 0.8871 | 0.8835 | 0.8862 |
| Llama 3 - FT | 62.57% | 0.6512 | 0.7965 | 0.9458 | 0.9469 | 0.9459 | 0.9466 |
Training Infos: The following hyperparameters were used during training:
| Parameter | Value |
|---|---|
| learning_rate | 1e-4 |
| weight_decay | 0.001 |
| train_batch_size | 16 |
| eval_batch_size | 8 |
| seed | 42 |
| optimizer | Adam - adamw_8bit |
| lr_scheduler_type | cosine |
| num_epochs | 3.0 |
QLoRA hyperparameters The following parameters related with the Quantized Low-Rank Adaptation and Quantization were used during training:
| Parameter | Value |
|---|---|
| lora_r | 16 |
| lora_alpha | 64 |
| lora_dropout | 0 |
Framework versions
| Library | Version |
|---|---|
| accelerate | 0.21.0 |
| bitsandbytes | 0.42.0 |
| Datasets | 2.14.3 |
| peft | 0.4.0 |
| Pytorch | 2.0.1 |
| safetensors | 0.4.1 |
| scikit-image | 0.22.0 |
| scikit-learn | 1.3.2 |
| Tokenizers | 0.14.1 |
| Transformers | 4.37.2 |
| trl | 0.4.7 |